We present a new approach to machine learning-powered combinatorial auctions, which is based on the principles of Differential Privacy. Our methodology guarantees that the auction mechanism is truthful, meaning that rational bidders have the incentive to reveal their true valuation functions. We achieve this by inducing truthfulness in the auction dynamics, ensuring that bidders consistently provide accurate information about their valuation functions. Our method not only ensures truthfulness but also preserves the efficiency of the original auction. This means that if the initial auction outputs an allocation with high social welfare, our modified truthful version of the auction will also achieve high social welfare. We use techniques from Differential Privacy, such as the Exponential Mechanism, to achieve these results. Additionally, we examine the application of differential privacy in auctions across both asymptotic and non-asymptotic regimes.

翻译：我们提出了一种基于差分隐私原则的机器学习驱动组合拍卖新方法。该方法保证了拍卖机制的真实性，即理性投标人有动机披露其真实估值函数。我们通过在拍卖动态中诱导真实性来实现这一目标，确保投标人始终提供关于其估值函数的准确信息。我们的方法不仅保证了真实性，还保留了原始拍卖的效率。这意味着，如果初始拍卖输出的分配具有高社会福利，则我们修改后的真实版本拍卖同样能实现高社会福利。我们采用差分隐私技术（如指数机制）来实现这些结果。此外，我们还研究了差分隐私在拍卖中渐进与非渐进场景下的应用。